75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
import torch
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import warnings
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from diffusers import AutoPipelineForText2Image
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from latentblending.blending_engine import BlendingEngine
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from lunar_tools import concatenate_movies
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import numpy as np
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torch.set_grad_enabled(False)
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torch.backends.cudnn.benchmark = False
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warnings.filterwarnings('ignore')
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import json
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# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
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# pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
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pipe = AutoPipelineForText2Image.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
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pipe.to('cuda')
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be = BlendingEngine(pipe, do_compile=False)
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fp_movie = f'test.mp4'
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fp_json = "movie_240221_1520.json"
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duration_single_trans = 10
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# Load the JSON data from the file
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with open(fp_json, 'r') as file:
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data = json.load(file)
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# Set up width, height, num_inference steps
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width = data[0]["width"]
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height = data[0]["height"]
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num_inference_steps = data[0]["num_inference_steps"]
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be.set_dimensions((width, height))
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be.set_num_inference_steps(num_inference_steps)
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# Initialize lists for prompts, negative prompts, and seeds
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list_prompts = []
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list_negative_prompts = []
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list_seeds = []
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# Extract prompts, negative prompts, and seeds from the data
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for item in data[1:]: # Skip the first item as it contains settings
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list_prompts.append(item["prompt"])
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list_negative_prompts.append(item["negative_prompt"])
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list_seeds.append(item["seed"])
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list_movie_parts = []
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for i in range(len(list_prompts) - 1):
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# For a multi transition we can save some computation time and recycle the latents
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if i == 0:
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be.set_prompt1(list_prompts[i])
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be.set_negative_prompt(list_negative_prompts[i])
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be.set_prompt2(list_prompts[i + 1])
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recycle_img1 = False
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else:
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be.swap_forward()
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be.set_negative_prompt(list_negative_prompts[i+1])
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be.set_prompt2(list_prompts[i + 1])
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recycle_img1 = True
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fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4"
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fixed_seeds = list_seeds[i:i + 2]
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# Run latent blending
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be.run_transition(
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recycle_img1=recycle_img1,
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fixed_seeds=fixed_seeds)
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# Save movie
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be.write_movie_transition(fp_movie_part, duration_single_trans)
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list_movie_parts.append(fp_movie_part)
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# Finally, concatente the result
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concatenate_movies(fp_movie, list_movie_parts)
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print(f"DONE! MOVIE SAVED IN {fp_movie}") |